Abstract
The work describes developments in the multiple regression performed for building models resistant to multicollinearity, having meaningful robust solution for individual parameters, convenient for interpretation of the results, and good for prediction. A tool from the cooperative game theory, the Shapley Value analysis, have been tried for estimation of regression coefficients and relative usefulness of the predictors in a model. This approach has been checked and successfully applied in various real-life projects in data analysts for commercial companies. It is useful for decision makers in economics, management, marketing research, and any other practical fields.
Subject
Applied Mathematics,Modelling and Simulation,Statistics and Probability
Reference18 articles.
1. Entropy Criterion in Logistic Regression and Shapley Value of Predictors;Lipovetsky;Journal of Modern Applied Statistical Methods,2006
2. Iteratively Re-weighted Random-Coefficient Models and Shapley Regression;Lipovetsky;Model Assisted Statistics and Applications,2007
3. Linear Regression with Special Coefficient Features Attained via Parameterization in Exponential, Logistic, and Multinomial – Logit Forms;Lipovetsky;Mathematical and Computer Modelling,2009
4. Meaningful regression coefficients built by data gradients;Lipovetsky;Advances in Adaptive Data Analysis,2010
5. Lipovetsky, S. (2012). Interpretation of Shapley Value Regression Coefficients as Approximation for Coefficients Derived by Elasticity Criterion, JSM’12, Proceedings of the Joint Statistical Meeting of the American Statistical Association, July-August, 3302-3307, San Diego, CA.
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